[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

scripts/backtest_uk_gsp.py

@@ -38,14 +38,11 @@
     NumpyBatch,
     batch_to_tensor,
     copy_batch_to_device,
-    stack_np_examples_into_batch,
 )
 from ocf_datapipes.config.load import load_yaml_configuration
 from ocf_datapipes.load import OpenGSP
-from ocf_datapipes.training.pvnet_all_gsp import (
-    create_t0_datapipe, construct_sliced_data_pipeline
-)
 from ocf_datapipes.training.common import _get_datapipes_dict
+from ocf_datapipes.training.pvnet_all_gsp import construct_sliced_data_pipeline, create_t0_datapipe
 from ocf_datapipes.utils.consts import ELEVATION_MEAN, ELEVATION_STD
 from omegaconf import DictConfig
 
@@ -201,7 +198,7 @@ def get_times_datapipe(config_path):
 
     # Set up ID location query object
     ds_gsp = get_gsp_ds(config_path)
-    gsp_id_to_loc = GSPLocationLookup(ds_gsp.x_osgb, ds_gsp.y_osgb)
+    GSPLocationLookup(ds_gsp.x_osgb, ds_gsp.y_osgb)
 
     # Filter the init-times to times we have all input data for
     available_target_times = get_available_t0_times(
@@ -368,7 +365,6 @@ def get_datapipe(config_path: str) -> NumpyBatch:
     # Convert to tensor for model
     data_pipeline = data_pipeline.map(batch_to_tensor).set_length(len(t0_datapipe))
 
-
     return data_pipeline
 
 

scripts/save_concurrent_batches.py

@@ -32,20 +32,17 @@
 import hydra
 import numpy as np
 import torch
-from ocf_datapipes.batch import BatchKey, batch_to_tensor, stack_np_examples_into_batch
-from ocf_datapipes.training.common import (
-    open_and_return_datapipes,
-)
+from ocf_datapipes.batch import BatchKey
 from ocf_datapipes.training.pvnet_all_gsp import (
-    construct_time_pipeline, construct_sliced_data_pipeline
+    construct_sliced_data_pipeline,
+    construct_time_pipeline,
 )
 from omegaconf import DictConfig, OmegaConf
 from sqlalchemy import exc as sa_exc
 from torch.utils.data import DataLoader
 from torch.utils.data.datapipes.iter import IterableWrapper
 from tqdm import tqdm
 
-
 warnings.filterwarnings("ignore", category=sa_exc.SAWarning)
 
 logger = logging.getLogger(__name__)
@@ -63,7 +60,6 @@ def __call__(self, input):
 
 
 def _get_datapipe(config_path, start_time, end_time, n_batches):
-
     t0_datapipe = construct_time_pipeline(
         config_path,
         start_time,
@@ -72,7 +68,7 @@ def _get_datapipe(config_path, start_time, end_time, n_batches):
 
     t0_datapipe = t0_datapipe.header(n_batches)
     t0_datapipe = t0_datapipe.sharding_filter()
-    
+
     datapipe = construct_sliced_data_pipeline(
         config_path,
         t0_datapipe,